Main Source Of Variation Research Articles

EC50 images, a novel endpoint from PET target occupancy studies, reveal spatial variation in apparent drug affinity.

We recently introduced voxel-level images of drug occupancy from PET via our "Lassen plot filter." Occupancy images revealed clear dependence of 11C-flumazenil displacement on dose of GABAa inhibitor, CVL-865, but with different scales in different brain regions. We hypothesized that regions requiring higher drug concentrations to achieve desired occupancy would have higher EC50 values. We introduce an "EC50 image" from human data to evaluate this hypothesis. Five healthy subjects were scanned with the nonselective GABAa tracer, 11C-flumazenil, before and (twice) after administration of CVL-865. We created ten occupancy images and applied an Emax model locally to create one EC50 image. We also performed simulations to confirm our observations of regional variation in EC50 and to identify the main source of variability in EC50. As expected, the EC50 image revealed spatial variation in apparent drug affinity. High EC50 was found in areas of low occupancy for a given drug dose. Simulations demonstrated that sampling from an inadequate range of plasma drug concentrations could impair precision. Our results argue for (a) confidence in the ability of the EC50 images to identify regional differences and (b) a need to tailor the range of drug doses in an occupancy study to regularize the precision of the EC50 throughout the brain. The EC50 image could add value to early-phase drug development by identifying regional variation in affinity that might impact therapy or safety and by guiding dose selection for later-phase trials.

Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2*R47H.

Late-onset Alzheimer’s disease (AD; LOAD) is the most common human neurodegenerative disease, however, the availability and efficacy of disease-modifying interventions is severely lacking. Despite exceptional efforts to understand disease progression via legacy amyloidogenic transgene mouse models, focus on disease translation with innovative mouse strains that better model the complexity of human AD is required to accelerate the development of future treatment modalities. LOAD within the human population is a polygenic and environmentally influenced disease with many risk factors acting in concert to produce disease processes parallel to those often muted by the early and aggressive aggregate formation in popular mouse strains. In addition to extracellular deposits of amyloid plaques and inclusions of the microtubule-associated protein tau, AD is also defined by synaptic/neuronal loss, vascular deficits, and neuroinflammation. These underlying processes need to be better defined, how the disease progresses with age, and compared to human-relevant outcomes. To create more translatable mouse models, MODEL-AD (Model Organism Development and Evaluation for Late-onset AD) groups are identifying and integrating disease-relevant, humanized gene sequences from public databases beginning with APOEε4 and Trem2*R47H, two of the most powerful risk factors present in human LOAD populations. Mice expressing endogenous, humanized APOEε4 and Trem2*R47H gene sequences were extensively aged and assayed using a multi-disciplined phenotyping approach associated with and relative to human AD pathology. Robust analytical pipelines measured behavioral, transcriptomic, metabolic, and neuropathological phenotypes in cross-sectional cohorts for progression of disease hallmarks at all life stages. In vivo PET/MRI neuroimaging revealed regional alterations in glycolytic metabolism and vascular perfusion. Transcriptional profiling by RNA-Seq of brain hemispheres identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes. Similarly, age was the strongest determinant of behavioral change. In the absence of mouse amyloid plaque formation, many of the hallmarks of AD were not observed in this strain. However, as a sensitized baseline model with many additional alleles and environmental modifications already appended, the dataset from this initial MODEL-AD strain serves an important role in establishing the individual effects and interaction between two strong genetic risk factors for LOAD in a mouse host.

First Palestinian intifada and intergenerational transmission of human capital

This paper attempts to estimate the intergenerational transmission of human capital in Palestine. The main question is whether formal parental education improves their offspring’s cognitive skills and school achievements. I use the instrumental variable method in the estimations to overcome the potential endogeneity of parental education. The main source of variation in parental educational attainment is parents’ exposure to the First Palestinian Intifada (1988–1993) during their middle and high school ages. During the First Palestinian Intifada, many school days were lost due to frequent school closures and other restrictions. Furthermore, many young people preferred to search for low-skill employment in Israel, since it provided them with better wages than the local labour market and hardly required any level of educational attainment. This study employs two outcomes, namely the standardised cognitive test scores and school achievements during the academic year 2012/2013 for students between grade 5 and grade 9 in West Bank schools. Overall, the results support the hypothesis of a human capital spill-over but more so for girls than for boys, where the instrumental variables results are often insignificant because of their large standard errors.

Sources of phenotypic variation of wood density and relationships with mean growth in two Eucalyptus species in Argentina

Aims of the study: To describe the radial patterns of wood density, and to identify their main sources of variation, and the potential tradeoffs with mean tree growth, in two Eucalyptus species. Area of study: Mesopotamian (Corrientes and Entre Ríos provinces) and Pampean region (Buenos Aires province) of Argentina. Materials and methods: Eucalyptus grandis and Eucalyptus viminalis, growing in genetic trials installed in two sites per species were studied. X-ray wood microdensity profiles were developed from core samples. Each profile was proportionally divided in 10 sections. Mean, maximum, minimum and the standard deviation of wood density, for each section were computed. Mean annual growth was used to study the relationships with wood microdensity variables. A linear mixed-effects model computed the significance of different sources of phenotypic variation. Pearson´s correlation computed the relationships between variables. Main results: The pattern of radial variation in E. grandis showed a decrease in wood density from pith to bark, mainly due to the decrease in minimum wood density, while in E. viminalis, wood density increased towards the outer wood. In both species, the standard deviation of the wood density increased along the radial profile from pith to bark. Significant variation in wood density was explained by site, provenance and clone/family effects. In E. grandis mean, maximum and minimum wood density were negatively correlated with mean growth, whereas in E. viminalis correlations were positive but close to zero. Research highlights: Both the pattern of radial variation of wood density and the relationship between wood density and mean growth were different in the studied Eucalyptus species, and they varied within species depending on the site they were growing and genetic provenance. Keywords: wood microdensity profile; wood properties; wood products; phenotypic plasticity; Eucalyptus grandis; Eucalyptus viminalis.

A New Pipeline for the Normalization and Pooling of Metabolomics Data.

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Use of Wild Relatives in Durum Wheat (Triticum turgidum L. var. durum Desf.) Breeding Program: Adaptation and Stability in Context of Contrasting Environments in Tunisia

In Mediterranean regions, the performance of durum wheat (Triticum turgidum L. var. durum Desf.) yield often varies due to significant genotype × environment interaction (GEI); therefore, yield stability is an important consideration in breeding programs. The aim of this research was to explore the GEI pattern and yield stability of 24 promising durum wheat lines, selected by ICARDA in several African countries (seven elites, four commercial varieties, and 13 durum wheat wide crosses, generated by hybridization of elites and Triticum dicoccoides Koern. ex Schweinf., Triticum araraticum Jakubz, and Aegilops speltoides Tausch) against a Tunisian local check variety ‘Salim’. Yield assessment was conducted across six environments under rainfed conditions, at the field station of Kef in a semi-arid region during four cropping seasons (2014–2015, 2015–2016, 2016–2017, and 2017–2018) and in a sub-humid region at the station of Beja during two cropping seasons (2015–2016 and 2018–2019). The analysis of variance showed that the environment is the main source of variation of grain yield (72.05%), followed by the interaction environments × genotypes (25.33%) and genotypes (2.62%). The genotype × genotype by environment model (PC) based on grain yield identified a mega-environment including Kef (2016–2017 and 2017–2018) and Beja (2015–2016 and 2018–2019) and elite line 22 as a widely adapted genotype. Combined analysis, computed using the average grain yield of lines and the yield stability wide adaptation index (AWAI), showed that elite lines 9 and 23 (2.41 and 2.34 t·ha−1, respectively), and wild relative-derived lines, 5, 1, and 10 (2.37, 2.31, and 2.28 t·ha−1, respectively) were more stable and better yielding than the national reference (2.21 t·ha−1). This finding supports the good yield potential of wild relative-derived lines. The five selections are recommended to be developed in multi-environments in several regions of Tunisia, especially in semi-arid area.

Predicting the outcomes of management strategies for controlling invasive river fishes using individual‐based models

Abstract The effects of biological invasions on native biodiversity have resulted in a range of policy and management initiatives to minimize their impacts. Although management options for invasive species include eradication and population control, empirical knowledge is limited on how different management strategies affect invasion outcomes. An individual‐based model (IBM) was developed to predict how different removal (‘culling’) strategies affected the abundance and spatial distribution of a virtual, small‐bodied, r‐selected alien fish (based on bitterling, Rhodeus sericeus) across three types of virtual river catchments (low/intermediate/high branching tributary configurations). It was then applied to nine virtual species of varying life‐history traits (r‐ to K‐selected) and dispersal abilities (slow/intermediate/fast) to identify trade‐offs between the management effort applied in the strategies (as culling rate and the number of patches it was applied to) and their predicted effects. It was also applied to a real‐world example, bitterling in the River Great Ouse, England. The IBM predicted that removal efforts were more effective when applied to recently colonized patches. Increasing the cull rate (proportion of individuals removed per patch), and its spatial extent was effective at controlling the invasive population; when both were relatively high, population eradication was predicted. The characteristics of the nine virtual species were the main source of variation in their predicted abundance and spatial distribution. No species were eradicated at cull rates below 70%. Eradication at higher cull rates depended on dispersal ability; slow dispersers required lower rates than fast dispersers, and the latter rapidly recolonized at low cull rates. The trade‐offs between management effort and the outcomes of the invasion were, generally, optimal when intermediate effort was applied to intermediate numbers of patches. In the Great Ouse, model predictions were that management interventions could restrict bitterling distribution by 2045 to 21% of the catchment (versus 90% occupancy without management). Synthesis and application. This IBM predicted how management efforts can be optimized against invasive fishes, providing a strong complement to risk assessments. We demonstrated that for a range of species' characteristics, culling can control and even eradicate invasive fish, but only if consistent and relatively high effort is applied.

Morphofunctional characteristics of the tomato male gametophyte under conditions of viral pathogenesis

Relevance. The action of viral phytopathogens changes the quality of the male gametophyte, which ultimately leads to a decrease in plant productivity. So, information on the features of the variability of morphofunctional characteristics of the microgametophyte becomes topical. Taking into account the expression of a part of the plant genome in pollen, the male gametophyte can be used as a reliable system for assessing the response of genotypes to the action of viral pathogens. Presuming that the problems of variability of characteristics of the male gametophyte in conditions of viral pathogenesis have not been enough studied, as well as the main factors determining the variability have not been identified, the aim of the research was to study the effect of viral phytopathogens on the male gametophyte characters in tomato.Material and methods. Hybrids F1 and varieties of tomato were used in the experiments. Plants were grown in a greenhouse. They were inoculated with tobacco mosaic virus (TMV) or tomato aspermia virus (TAV) in the 4-5 leaf phase. To determine the quality of pollen, flowers of control and infected plants were collected; pollen was isolated and sown on a nutrient medium. The preparations were analyzed under microscope. The viability of the pollen and the length of the pollen tubes were assessed, and the ratio of these indicators was calculated.Results. Infection of plants with viruses changes some functional characteristics of the male gametophyte. The responses manifested as stimulation, inhibition, or neutral effect. The main sources of variability in characters of pollen were the genotype and viral agents. In viral pathogenesis, unequal indicators of pollen grains were found in terms of the rate of germination and growth of pollen tubes (PT). Analysis of the heritability coefficients of the ratio of pollen viability and PT length (experiment / control) revealed positive dominance and overdominance in 70% of cases. Genotypes have been identified that combine a high level of viability when infected with different viruses. The prospect of their use in further research is proposed.

Simulating upland rice yield at diverse temperatures using DSSAT4.7-CERES-Rice crop model under changing climatic conditions in southern Thailand

Declaration of climate emergency reminds that the main source of variations in worldwide food production sourced from developing countries and is of utmost concern. Process-based models use simplified functions to express the interactions between upland rice growth and the major environmental factors (i.e., climate, soils, and management) that affect crops yield. This research was conducted to investigate the DSSAT-CERES-Rice model for simulating the impact of different temperature (28˚C, 30˚C and 32˚C) on upland grain yield (Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm). The results showed that the temperature significantly affected the grain yield, flowering, and maturity date. The highest grain yield bearing genotype was Bow leb Nahng (6235.80 kgha -1 ) with the highest variation between the genotypes. At maximum temperature 32˚C simulated grain yield varied from (3194-6669) kgha -1 , at high temperature 30˚C it varied from 3252-6667 kgha -1 while at moderate temperature 28˚C 3189-6711 kgha -1 was the observed range. However, results indicated that moderate temperature i.e., 28˚C gave the highest simulated grain yield. Thus, it was demonstrated that the CERES-Rice model was more useful as a tool for simulating grain yield under the changing different temperature conditions.

Proteomic comparison between different tissue preservation methods for identification of promising biomarkers of urothelial bladder cancer

Samples in biobanks are generally preserved by formalin-fixation and paraffin-embedding (FFPE) and/or optimal cutting temperature compound (OCT)-embedding and subsequently frozen. Mass spectrometry (MS)-based analysis of these samples is now available via developed protocols, however, the differences in results with respect to preservation methods needs further investigation. Here we use bladder urothelial carcinoma tissue of two different tumor stages (Ta/T1—non-muscle invasive bladder cancer (NMIBC), and T2/T3—muscle invasive bladder cancer (MIBC)) which, upon sampling, were divided and preserved by FFPE and OCT. Samples were parallel processed from the two methods and proteins were analyzed with label-free quantitative MS. Over 700 and 1200 proteins were quantified in FFPE and OCT samples, respectively. Multivariate analysis indicates that the preservation method is the main source of variation, but also tumors of different stages could be differentiated. Proteins involved in mitochondrial function were overrepresented in OCT data but missing in the FFPE data, indicating that these proteins are not well preserved by FFPE. Concordant results for proteins such as HMGCS2 (uniquely quantified in Ta/T1 tumors), and LGALS1, ANXA5 and plastin (upregulated in T2/T3 tumors) were observed in both FFPE and OCT data, which supports the use of MS technology for biobank samples and encourages the further evaluation of these proteins as biomarkers.

White matter abnormalities are key components of cerebrovascular disease impacting cognitive decline.

While cerebrovascular disease can be observed in vivo using MRI, the multiplicity and heterogeneity in the mechanisms of cerebrovascular damage impede accounting for these measures in ageing and dementia studies. Our primary goal was to investigate the key sources of variability across MRI markers of cerebrovascular disease and evaluate their impact in comparison to amyloidosis on cognitive decline in a population-based sample. Our secondary goal was to evaluate the prognostic utility of a cerebrovascular summary measure from all markers. We included both visible lesions seen on MRI (white matter hyperintensities, cortical and subcortical infarctions, lobar and deep microbleeds) and early white matter damage due to systemic vascular health using diffusion changes in the genu of the corpus callosum. We identified 1089 individuals aged ≥60 years with concurrent amyloid-PET and MRI scans from the population-based Mayo Clinic Study of Aging. We divided these into discovery and validation datasets. Using the discovery dataset, we conducted principal component analyses and ascertained the main sources of variability in cerebrovascular disease markers. Using linear regression and mixed effect models, we evaluated the utility of these principal components and combinations of these components for the prediction of cognitive performance along with amyloidosis. Our main findings were (i) there were three primary sources of variability among the CVD measures—white matter changes are driven by white matter hyperintensities and diffusion changes; number of microbleeds (lobar and deep); and number of infarctions (cortical and subcortical); (ii) Components of white matter changes and microbleeds but not infarctions significantly predicted cognition trajectories in all domains with greater contributions from white matter; and (iii) The summary vascular score explained 3–5% of variability in baseline global cognition in comparison to 3–6% variability explained by amyloidosis. Across all cognitive domains, the vascular summary score had the least impact on memory performance (∼1%). Though there is mechanistic heterogeneity in the cerebrovascular disease markers measured on MRI, these changes can be grouped into three components and together explain variability in cognitive performance equivalent to the impact of amyloidosis on cognition. White matter changes represent dynamic ongoing damage, predicts future cognitive decline across all domains and diffusion measurements help capture white matter damage due to systemic vascular changes. Therefore, measuring and accounting for white matter changes using diffusion MRI and white matter hyperintensities along with microbleeds will allow us to capture vascular contributions to cognitive impairment and dementia.

Embodied energy and carbon of building insulating materials: A critical review

An extensive and updated review of 223 values about the embodied energy and carbon of building insulation materials, mainly retrieved from 156 Environmental Product Declarations, is proposed with attention to innovative and emerging insulation materials. Comparative analyses were carried out thanks to the adoption of the same functional unit (1 m 2 with a thermal resistance of 1m 2 K/W and a design life span of 50 years) to assess the consistence of reference values and the main sources of variability. The comparison suffers from many uncertainties and variance decomposition was employed to verify the main drivers of variability in the dataset elaborated. Data about traditional insulation materials show the lowest ranges of variation when reported per functional unit while the definition of reference values for innovative and unconventional insulation materials was quite difficult due to the low values found and their high variance. Traditional inorganic insulation materials exhibit competitive embodied impacts (glass wool: 16–31 MJ/FU and 0.6–1.2 kg CO 2 eq/FU; stone wool: 21–66 MJ/FU and 1.4–4.2 kg CO 2 eq/FU) if compared with fossil fuel derived (EPS: 44–78 MJ/FU and 1.9–3.5 kg CO 2 eq/FU) or many emerging super-insulating solutions (aerogel: 251–372 MJ/FU and 11.6–18.7 kg CO 2 eq/FU). Density is an important variability carrier for glass wool, stone wool, and EPS. • We got 223 values of PER, PENR, GWP of building insulation panels from 156 EPDs • The values were compared considering the same functional unit (1 m 2 with R = 1m 2 K/W). • Wide ranges of variation were obtained for some insulation materials. • The values of unconventional insulation materials show the highest uncertainty. • Density is a significant variability carrier for EPS, stone wool and glass wool.

Characterisation of wood quality of Eucalyptus nitens plantations and predictive models of density and stiffness with site and tree characteristics

Wood density and stiffness are among the most important wood quality traits for timber products and the prediction of these traits in trees planted across large geographic areas is of increasing interest for the forest industry. This study was conducted across 65 stands of E. nitens fibre-grown plantations, the most widely planted hardwood species in Tasmania, Australia, and aimed to (i) identify the main sources of variability in density (basic density, ρb) and stiffness (dynamic modulus of elasticity, Ed) of E. nitens fibre-grown trees, (ii) identify site, climatic, environmental and geomorphological factors across the E. nitens estate and tree characteristics at the site level that influence ρb and Ed of the trees, and based on this information (iii) develop models to predict the two wood properties at the tree and site level. An extensive dataset (n = 1880) of tree ρb and Ed measures was obtained through direct tree wood sampling for basic density and non-destructive acoustic assessments for stiffness. Large variation in tree density and stiffness was found across the study sites and among trees, with basic density ranging from 0.36 g cm−3 to 0.65 g cm−3 and stiffness from 9.2 GPa to 24.7 GPa. Candidate models to predict the two properties were developed on a training dataset and validated on a separate part of the dataset. The best tree-level models describing the variation of ρb and Ed included both tree and site factors. The best model describing ρb variation on trees included tree diameter, maximum temperature of the warmest period and precipitation of the wettest period as explanatory variables, while the best Ed model for trees included tree slenderness, height and basic density and the climatic variables, elevation and site index. The predictive models explained 31% of the total variability of ρb and 59% of the total variability of Ed across the validation sites. At the site level, the best models for density and stiffness included forest structure, climatic, and geomorphological variables, as well as plot location. The model for site density explained 73% of the variability of average density, while the model for site stiffness explained 35% of the variability of average stiffness.These findings demonstrate that different site and tree factors influence the two commercially important wood quality traits, and these can be used in combination to satisfactorily predict tree basic density and stiffness across different environments both at the tree and site level. This knowledge can be employed to support the forest and wood processing industry to expand the possible uses of E. nitens fibre-grown trees, to better manage the harvest for specific products, and to develop models for planning next generation plantings.

Diversidad morfológica del mezquite (Prosopis spp.) en la región de Pénjamo, Guanajuato, México

Objetivo: evaluar la amplitud de la variabilidad morfológica del mezquite de la región de Pénjamo, en función de las características con mayor valor descriptivo.Método: a partir de 32 colectas de mezquite en siete localidades de Pénjamo, Guanajuato, se midieron 20 caracteres morfológicos cualitativos (7) y cuantitativos (13) que fueron analizados por componentes principales (ACP) y conglomerados (AC).Resultados: la caracterización morfológica permitió diferenciar dos grupos y cinco subgrupos basados en el tamaño de la vaina y área foliar.Limitaciones: considerar factores bióticos, abióticos y antropogénicos que puedan contribuir a explicar la variación observada.Principales hallazgos: la longitud y ancho del foliolo, pinna y vaina son los principales contribuyentes a la variación cuantitativa, mientras que en características cualitativas la fuente principal de variación fue la posición y arreglo de las espinas, así como la presencia de flores. El ACP indica que el tipo de suelo del lugar de colecta se asoció a la variación cualitativa del mezquite.

Nitrogen nutrition index and forage yield explain nitrogen utilization efficiency in hybrid ryegrasses under different nitrogen availabilities

Abstract Improving nitrogen (N) utilization efficiency (NUtE (kg dry matter (DM) kg−1 N uptake)) is one of the challenges of modern grassland systems in order to improve nitrogen use efficiency. The NUtE is highly variable in response to climate and N supply. Therefore, NUtE must be assessed in relation to the N nutrition index (NNI) and forage yield in pastures growing under different N supplies and environments. The aim of this study was to assess NUtE and NNI in response to different ryegrass genotypes, N supplies and harvest times. Two experiments were conducted in southern Chile for this purpose. In each experiment, treatments were the factorial combination of two hybrid ryegrass cultivars (Shogun and Trojan) and seven N fertilization rates (0, 50, 100, 200, 350, 525 and 700 kg N ha−1 yr−1). A wide range in N uptake, NUtE and NNI were observed (N uptake: 4 – 134 kg N ha−1; NUtE: 20 – 71 kg DM kg−1 N uptake; NNI: 0.23 – 1.43) across experiments. The main sources of variation of the above-mentioned variables were N rates and harvest times. Across experiments, the NUtE was described by a negative power function (R2 = 0.77, P

Investigating Complex Samples with Molograms of Low-Affinity Binders.

In vitro diagnostics relies on the quantification of minute amounts of a specific biomolecule, called biomarker, from a biological sample. The majority of clinically relevant biomarkers for conditions beyond infectious diseases are detected by means of binding assays, where target biomarkers bind to a solid phase and are detected by biochemical or physical means. Nonspecifically bound biomolecules, the main source of variation in such assays, need to be washed away in a laborious process, restricting the development of widespread point-of-care diagnostics. Here, we show that a diffractometric assay provides a new, label-free possibility to investigate complex samples, such as blood plasma. A coherently arranged sub-micron pattern, that is, a peptide mologram, is created to demonstrate the insensitivity of this diffractometric assay to the unwanted masking effect of nonspecific interactions. In addition, using an array of low-affinity binders, we also demonstrate the feasibility of molecular profiling of blood plasma in real time and show that individual patients can be differentiated based on the binding kinetics of circulating proteins.

Utilization of biological variation data in the interpretation of laboratory test results - survey about clinicians' opinion and knowledge.

IntroductionTo interpret test results correctly, understanding of the variations that affect test results is essential. The aim of this study is: 1) to evaluate the clinicians’ knowledge and opinion concerning biological variation (BV), and 2) to investigate if clinicians use BV in the interpretation of test results.Materials and methodsThis study uses a questionnaire comprising open-ended and close-ended questions. Questions were selected from the real-life numerical examples of interpretation of test results, the knowledge about main sources of variations in laboratories and the opinion of clinicians on BV. A total of 399 clinicians were interviewed, and the answers were evaluated using a scoring system ranked from A (clinician has the highest level of knowledge and the ability of using BV data) to D (clinician has no knowledge about variations in laboratory). The results were presented as number (N) and percentage (%).ResultsAltogether, 60.4% of clinicians have knowledge of pre-analytical and analytical variations; but only 3.5% of them have knowledge related to BV. The number of clinicians using BV data or reference change value (RCV) to interpret measurements results was zero, while 79.4% of clinicians accepted that the difference between two measurements results located within the reference interval may be significant.ConclusionsClinicians do not use BV data or tools derived from BV such as RCV to interpret test results. It is recommended that BV should be included in the medical school curriculum, and clinicians should be encouraged to use BV data for safe and valid interpretation of test results.

A Comparative Study for Assessing the Drought-Tolerance of Chickpea Under Varying Natural Growth Environments.

This study was planned with the purpose of evaluating the drought tolerance of advanced breeding lines of chickpea in natural field conditions. Two methods were employed to impose field conditions; the first: simulating drought stress by growing chickpea genotypes at five rainfed areas, with Faisalabad as the non-stressed control environment; and the second: planting chickpea genotypes in spring to simulate a drought stress environment, with winter-sowing serving as the non-stressed environment. Additive main effects and multiplicative interaction (AMMI) and generalized linear models (GLM) models were both found to be equally effective in extracting main effects in the rainfed experiment. Results demonstrated that environment influenced seed yield, number of primary and secondary branches, number of pods, and number of seeds most predominantly; however, genotype was the main source of variation in 100 seed weight and plant height. The GGE biplot showed that Faisalabad, Kallur Kot, and Bhakkar were contributing the most in the GEI, respectively, while Bahawalpur, Bhawana, and Karor were relatively stable environments, respectively. Faisalabad was the most, and Bhakkar the least productive in terms of seed yield. The best genotypes to grow in non-stressed environments were CH39/08, CH40/09, and CH15/11, whereas CH28/07 and CH39/08 were found suitable for both conditions. CH55/09 displayed the best performance in stress conditions only. The AMMI stability and drought-tolerance indices enabled us to select genotypes with differential performance in both conditions. It is therefore concluded that the spring-sown experiment revealed a high-grade drought stress imposition on plants, and that the genotypes selected by both methods shared quite similar rankings, and also that manually computed drought-tolerance indices are also comparable for usage for better genotypic selections. This study could provide sufficient evidence for using the aforementioned as drought-tolerance evaluation methods, especially for countries and research organizations who have limited resources and funding for conducting multilocation trials, and performing sophisticated analyses on expensive software.

Tracing the geographical origin of Spanish mango (Mangifera indica L.) using stable isotopes ratios and multi-element profiles

The stable isotopes of five bio-elements (δ13C, δ15N, δ34S, δ2H, δ18O) and the elemental composition profile combined with chemometrics were used to differentiate the geographical origin of commercial samples of mango, aimed at contributing to the existence of a Spanish protected geographical indication (PGI). Eighty commercial mangoes from all over the world (Spain, Mexico, Peru, Brazil, Ivory Coast, Equatorial Guinea and Senegal), collected during two consecutive harvests, were analysed and assessed by means of univariate and multivariate data analyses, establishing the main variation sources linked to the origin of the mangoes. The data obtained from each analytical platform were assessed separately and in combination, resulting in three models whose discriminatory power (Spanish vs others) was compared. The model fitted for the stable isotope approach yielded a sensitivity of 100%, meaning that the Spanish mangoes were correctly classified in all the cases, against the 97.6% for multi-element analysis. The combined use of stable isotopes and elemental composition led to the most accurate geographical discrimination of Spanish mangoes, making it possible to select three stable isotope ratios (δ15N, δ18O, δ34S) and six elements (Cd, Se, Cs, As, Pb and Ba) as the main indicators of the geographic origin of mango. The results of this study revealed the feasibility of tracing the provenance of mango through either of the two analytical strategies or by combining them to obtain a more accurate classification.

Source/Drain Extension Doping Engineering for Variability Suppression and Performance Enhancement in 3-nm Node FinFETs

In this article, variability suppression and performance enhancement through source/drain extension (SDE) module engineering is demonstrated in 3-nm node fin field-effect transistors (FinFETs). The process variabilities induced by different modules are systematically quantified in 3-nm node FinFETs using fully calibrated technology computer-aided design (TCAD) tools. With experimentally characterized geometry parameters and their variation ranges, the fin and the SDE module are identified as the main variability sources. The unique device performances induced by the realistic fin and SDE modules are interpreted. Previously developed for performance enhancement in 7- and 10-nm node FinFETs, 3-D SDE doping profile engineering can effectively reduce fin and SDE module-induced variabilities by $4.0\times $ ( $2.7\times $ ) and $1.8\times $ ( $2.1\times $ ) in 3-nm node n-(p-)FinFETs, respectively. In addition, the precise control of the vertical SDE profile enables a new dimension for design optimization. An optimization methodology for the 3-D SDE doping profile is demonstrated, achieving a 7% (10%) ON-state current enhancement in 3-nm node n-(p-)FinFETs.